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Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia
Dengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433680/ https://www.ncbi.nlm.nih.gov/pubmed/36061000 http://dx.doi.org/10.1016/j.heliyon.2022.e10350 |
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author | Fauzi, Ilham Saiful Nuraini, Nuning Ayu, Regina Wahyudyah Sonata Lestari, Bony Wiem |
author_facet | Fauzi, Ilham Saiful Nuraini, Nuning Ayu, Regina Wahyudyah Sonata Lestari, Bony Wiem |
author_sort | Fauzi, Ilham Saiful |
collection | PubMed |
description | Dengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and spatial distributions to obtain more obvious insights into its spatial-temporal evolution. We utilized the Richards model to estimate the growth rate and detect the peak (or turning point) of the dengue infection wave by identifying the temporal progression at each location. Using spatial analysis of geo-referenced data from a local perspective, we investigated the changes in the spatial clusters of dengue cases and detected hot spots and cold spots in each weekly cycle. We found that the trend of confirmed dengue incidences significantly increases from January to March. More than two-third (70.4%) of the regions in West Java had their dengue infection turning point ranging from the first week of January to the second week of March. This trend clearly coincides with the peak of precipitation level during the rainy season. Further, the spatial analysis identified the hot spots distributed across central, northern, northeastern, and southeastern regions in West Java. The densely populated areas were likewise seen to be associated with the high-risk areas of dengue exposure. Recognizing the peak of epidemic and geographical distribution of dengue cases might provide important insights that may help local authorities optimize their dengue prevention and intervention programs. |
format | Online Article Text |
id | pubmed-9433680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-94336802022-09-02 Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia Fauzi, Ilham Saiful Nuraini, Nuning Ayu, Regina Wahyudyah Sonata Lestari, Bony Wiem Heliyon Research Article Dengue fever is a notable vector-borne viral disease, currently becoming the most dreaded worldwide health problem in terms of the number of people affected. A data set of confirmed dengue incidences collected in the province of West Java has allowed us to explore dengue's temporal trends and spatial distributions to obtain more obvious insights into its spatial-temporal evolution. We utilized the Richards model to estimate the growth rate and detect the peak (or turning point) of the dengue infection wave by identifying the temporal progression at each location. Using spatial analysis of geo-referenced data from a local perspective, we investigated the changes in the spatial clusters of dengue cases and detected hot spots and cold spots in each weekly cycle. We found that the trend of confirmed dengue incidences significantly increases from January to March. More than two-third (70.4%) of the regions in West Java had their dengue infection turning point ranging from the first week of January to the second week of March. This trend clearly coincides with the peak of precipitation level during the rainy season. Further, the spatial analysis identified the hot spots distributed across central, northern, northeastern, and southeastern regions in West Java. The densely populated areas were likewise seen to be associated with the high-risk areas of dengue exposure. Recognizing the peak of epidemic and geographical distribution of dengue cases might provide important insights that may help local authorities optimize their dengue prevention and intervention programs. Elsevier 2022-08-25 /pmc/articles/PMC9433680/ /pubmed/36061000 http://dx.doi.org/10.1016/j.heliyon.2022.e10350 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Fauzi, Ilham Saiful Nuraini, Nuning Ayu, Regina Wahyudyah Sonata Lestari, Bony Wiem Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title_full | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title_fullStr | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title_full_unstemmed | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title_short | Temporal trend and spatial clustering of the dengue fever prevalence in West Java, Indonesia |
title_sort | temporal trend and spatial clustering of the dengue fever prevalence in west java, indonesia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433680/ https://www.ncbi.nlm.nih.gov/pubmed/36061000 http://dx.doi.org/10.1016/j.heliyon.2022.e10350 |
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